STORAGE SYSTEM WITH MACHINE LEARNING MECHANISM AND METHOD OF OPERATION THEREOF

• US 20190095299A1
• Filed: 09/28/2017
• Published: 03/28/2019
• Est. Priority Date: 09/28/2017
• Status: Abandoned Application

• Current Assignee: Point Financial, Inc.
• Original Assignee: Cnex Labs, Inc.
• Inventors: Liu, Yi, Zhang, Xiaojie, Armstrong, Alan
• Application Number:
  US15/718,795
• Publication Number:
  US 20190095299A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 11/2257 using expert systems
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G11C 2029/0411 Online error correction
• CPC Class Codes: G11C 29/44 Indication or identification of errors, e.g. for repair
• CPC Class Codes: G11C 29/76 using address translation or modifications

Abstract

A storage system includes: a control processor, configured to: read user data, calculate error statistics from the user data, and operate a machine learning mechanism configured to identify a bad sector based on the error statistics; and a non-volatile memory array, coupled to the control processor, configured to store the user data; and wherein the control processor is further configured to map out the bad sector, based on the machine learning mechanism, and move the user data to a target sector for enhancing performance of the non-volatile memory array.

Claims

1. A storage system comprising:

a control processor, configured to;

read user data,

calculate error statistics from the user data, and

operate a machine learning mechanism configured to identify a bad sector based on the error statistics; and

a non-volatile memory array, coupled to the control processor, configured to store the user data; and

wherein the control processor is further configured to map out the bad sector, based on the machine learning mechanism, and move the user data to a target sector for enhancing performance of the non-volatile memory array.

2. The system as claimed in claim 1 wherein the control processor is further configured to monitor the error statistics for each of the sector 0 through sector N.

3. The system as claimed in claim 1 wherein the control processor is further configured to refine the machine learning mechanism for determining a bad sector by monitoring the error statistics.

4. The system as claimed in claim 1 wherein the control processor is further configured to operate a program/erase (P/E) interval monitor to pass the error statistics to the machine learning mechanism at selected intervals of the P/E cycle.

5. The system as claimed in claim 1 wherein the control processor is further configured to calculate a non-linear component of the error statistics with past error statistics.

6. The system as claimed in claim 1 wherein the control processor is further configured to operate the machine learning mechanism by calculating a bad sector indicator.

7. The system as claimed in claim 1 wherein the control processor is configured to identify the bad sector includes comparing the error statistics to a bad sector threshold.

8. The system as claimed in claim 1 wherein the control processor is further configured to predict the bad sector includes calculating a non-linear component of the error statistic.

9. The system as claimed in claim 1 wherein the control processor is further configured to refine the machine learning mechanism when an uncorrectable error trigger is activated.

10. The system as claimed in claim 1 wherein the control processor is further configured to restore the machine learning mechanism to an initial state.

11. A method of operation of a storage system comprising:

reading user data from a non-volatile memory array;

calculating error statistics from the user data;

operating a machine learning mechanism with the error statistics;

identifying a bad sector by the machine learning mechanism; and

mapping out the bad sector including moving the user data to a target sector for enhancing performance of the non-volatile memory array.

12. The method as claimed in claim 11 wherein reading the user data includes monitoring the bit error count for each of the sector 0 through sector N.

13. The method as claimed in claim 11 further comprising refining the machine learning mechanism for determining a bad sector by monitoring the bit error count.

14. The method as claimed in claim 11 further comprising passing the error statistics to the machine learning mechanism at selected intervals of the P/E cycle.

15. The method as claimed in claim 11 further comprising calculating a non-linear component of the error statistics with past error statistics.

16. The method as claimed in claim 11 wherein operating the machine learning mechanism with the error statistics includes calculating a bad sector indicator.

17. The method as claimed in claim 11 wherein identifying the bad sector includes comparing the bit error count to a bad sector threshold.

18. The method as claimed in claim 11 further comprising calculating a non-linear component of the error statistic.

19. The method as claimed in claim 11 further comprising refining the machine learning mechanism when an uncorrectable error trigger is activated.

20. The method as claimed in claim 11 further comprising restoring the machine learning mechanism to an initial state.

21. A non-transitory computer readable medium including instructions for execution, the medium comprising:

reading user data from a non-volatile memory array;

calculating error statistics for the user data;

operating a machine learning mechanism with the error statistics;

identifying a bad sector by the machine learning mechanism; and

mapping out the bad sector including moving the user data to a target sector for enhancing performance of the non-volatile memory array.

22. The medium as claimed in claim 21 wherein reading the user data includes monitoring the bit error count for each of the sector 0 through sector N.

23. The medium as claimed in claim 21 further comprising refining the machine learning mechanism for determining a bad sector by monitoring the bit error count.

24. The medium as claimed in claim 21 further comprising passing the error statistics to the machine learning mechanism at selected intervals of the P/E cycle.

25. The medium as claimed in claim 21 further comprising calculating a non-linear component of the error statistics with past error statistics.

26. The medium as claimed in claim 21 wherein operating the machine learning mechanism with the error statistics includes calculating a bad sector indicator.

27. The medium as claimed in claim 21 wherein identifying the bad sector includes comparing the bit error count to a bad sector threshold.

28. The medium as claimed in claim 21 further comprising calculating a non-linear component of the error statistic.

29. The medium as claimed in claim 21 further comprising refining the machine learning mechanism when an uncorrectable error trigger is activated.

30. The medium as claimed in claim 21 further comprising restoring the machine learning mechanism to an initial state.